Many present-day communication systems provide unpleasant speech quality in the presence of background noise. These communications systems are not able to adequately distinguish background noise from voice information, with the result that the system attempts to transmit both voice and noise over a communications link. At the other end of the communications link, this transmitted noise degrades the quality of the received voice signal. Such degradations are particularly serious in the context of wireless communications systems. For example, consider digital cellular telephone transceivers which incorporate speech coders so as to reduce the bit rate that must be transmitted over the communications channel. Although bit rate reduction is desirable in that it permits the capacity of wireless communication systems to be increased, it renders the communications system increasingly more susceptible to speech quality degradations in the presence of noise.
As a practical matter, it is difficult or impossible to determine the actual waveform of background noise. Therefore, in order to reduce the extent to which background noise degrades speech quality, it is necessary to develop an estimate of the characteristics of this noise. The characteristics of noise can be expressed in terms of a plurality of noise parameters. For purposes of improving speech quality, an estimate of noise parameters will suffice.
The primary focus of prior art noise estimation techniques has been on the estimation of noise parameters during speech pauses. Indeed, it is much more difficult to estimate noise parameters in the presence of speech activity and, as such, the prior art includes fewer examples of techniques for addressing this more complex problem. One approach for estimating noise parameters is disclosed in U.S. Pat. No. 4,185,168 issued to D. Graupe and G. D. Causey on January 22, 1980 and entitled, “Method and Means for Adaptively Filtering Near-Stationary Noise From an Information Bearing Signal”. This patent describes a noise estimator that detects the minima of a plurality of successively smoothed input magnitude values. The smallest minimum out of a predefined number of minima is used as an estimate for the spectral magnitude of the noise. A major drawback of the Graupe-Causey noise estimator is the lack of an adaptation mechanism to prevent the noise estimate from jumping up rapidly. These rapid jumps may be a problem in cases where the estimator attempts to follow speech instead of noise. Moreover, the presence of such a mechanism is important in cases where no spectral minima of speech occur during the period over which spectral minima are evaluated.
A noise estimator that eliminates the drawback described in the preceding paragraph is disclosed in a dissertation entitled, “Contributions to Noise Suppression in Monophonic Speech Signals,” by Walter Etter, Ph.D. Thesis, ETH Zurich, 1993, available from the Swiss Federal Institute of Technology. This estimator, referred to as the “Two Time Parameter” (TTP) noise estimator, provides control over the attack time of the noise estimate through the use of a rise time limitation filter. Since the duration between spectral minima in speech follows a statistical pattern, no precise upper length for this duration can be specified. Therefore, a minimum tracker may provide, for example, a 95% certainty that it tracks speech minima. For the remaining 5%, the noise estimator would immediately attempt to follow high-level speech unless an appropriate mechanism, such as a rise time limitation filter, were in place to prevent immediate attacks and the consequent following of speech instead of noise.
Consider a more advanced TTP (two time parameter) noise estimator, which uses a minimum rise (MR) filter consisting of a minimum hold filter followed by a rise time limitation filter, where each of these filters is defined in terms of two time parameters related to the occurrence of phonemic minima in the speech signal. A significant drawback of the MR filter is its computational complexity. More specifically, the filter requires a step of successively selecting the smallest sample from a sequence of M samples, which is very demanding from a computational point of view. This complexity translates into higher product costs, requiring the use of a relatively elaborate digital signal processor, thereby reducing battery life in portable applications. Since, in general, a noise estimator is only part of an entire system, it is allocated only a small portion of the total signal processing power provided by a digital signal processor (DSP) integrated circuit. For the foregoing reasons, it is not economically feasible to implement an MR filter using present-day hardware. What is needed is a noise estimation technique that approximates the performance of an MR filter, while at the same time providing reduced computational complexity.
The prior art presents yet another shortcoming that relates to the accuracy of the noise estimate obtained from a signal. In the prior art, when noise and speech have similar magnitudes but substantially opposite phases, the noise and the speech cancel out, resulting in the occurrence of one or more minima in a set of samples. In the frequency domain, this set of samples may represent spectral magnitudes of a signal for which the noise component is to be estimated. By contrast, in the time domain, the sample set may represent short term average (or RMS) values of the signal. The noise estimation process is unable to distinguish minima attributable to phase cancellations from other minima that are attributable to the occurrence of phonemic minima in speech. However, the noise components of a signal can be estimated accurately during the occurrence of phonemic minima, but not during the aforementioned phase cancellations. The prior art approach mistakes phase cancellation minima for phonemic minima, resulting in an inaccurate estimate of noise. Note that prior art approaches, such as the previous two noise estimators described above, have addressed the phase cancellation problem in an ad-hoc way using a lowpass pre-filter. Such a filter merely smoothes out short minima across the set of samples instead of eliminating these minima from the samples. In addition, a lowpass pre-filter leads to a bias in the noise estimate, and it can be difficult or impossible to provide compensation for this bias. The prior art has failed to realize that this pre-filter plays an essential role in estimating noise from a speech signal, and that the use of a simple lowpass filter is not an adequate approach. Also, the prior art fails to provide an analysis of this phase cancellation problem in order to define an appropriate filter characteristic of the pre-filter.